• Assist with building and maintaining data pipelines to ensure smooth data integration and processing. • Support the development and optimization of ETL (Extract, Transform, Load) workflows. • Help in cleaning, organizing, and validating large datasets for analysis and reporting. • Collaborate with data engineers to troubleshoot and resolve data-related issues. • Work with various data storage and processing tools such as databases, cloud platforms, and scripting languages. • Participate in team meetings to discuss data solutions and contribute ideas for process improvement. • Perform other ad-hoc data engineering tasks and projects as assigned by senior team members.

• Collaborate with our Business Intelligence team to understand business reporting requirements and define denormalized schema for the warehouse • Collaborate with our DBA team on capacity planning and deploying infrastructure resources • Build and maintain ETL solutions that centralize data from disparate sources into a centralized data warehouse • Collaborate with DBA, Security and Infrastructure teams to establish secure connections between data tiers • Build and maintain ETL solutions that denormalize OLTP source data into reportable OLAP data • Collaborate with stakeholders to RCA and remediate data and reporting defects and inconsistencies • Establish monitoring and alerting solutions to support the data warehouse and related ETL jobs

• Implement robust data infrastructure in AWS, using Spark with Scala • Evolve our core data pipelines to efficiently scale for our massive growth • Store data in optimal engines and formats • Collaborate with our cross-functional teams to design data solutions that meet business needs • Built out fault-tolerant batch and streaming pipelines • Leverage and optimize AWS resources while designing for scale • Collaborate closely with our Data Science and Product teams

• Design and maintain a scalable identity resolution platform • Build pipelines and services to ingest, normalize, link, and version identity data across multiple sources • Ensure deterministic and probabilistic matching logic that is transparent, auditable, and measurable • Partner with product and analytics teams to expose identity data through reliable, well-documented APIs and datasets • Build and operate batch and streaming pipelines using modern data stack tools • Create clear documentation, standards, and runbooks for identity and governance systems • Own data governance foundations including data lineage, quality checks, schema enforcement, and access controls • Implement privacy-by-design principles (PII handling, consent enforcement, retention policies) • Collaborate with legal, privacy, and security teams to operationalize regulatory requirements (e.g., GDPR, CCPA) • Establish monitoring and alerting for data quality, freshness, and integrity